Tag Archives: analytics conference

I’ve been planning my schedule for the DA Hub in late September and while I find it frustrating (so much interesting stuff!), it’s also enlightening about where digital analytics is right now and where it’s headed. Every conference is a kind of mirror to its industry, of course, but that reflection is often distorted by the needs of the conference – to focus on the cutting-edge, to sell sponsorships, to encourage product adoption, etc. With DA Hub, the Conference agenda is set by the enterprise practitioners who are leading groups – and it’s what they want to talk about. That makes the conference agenda unusually broad and, it seems to me, uniquely reflective of the state of our industry (at least at the big enterprise level).

So here’s a guided tour of my DA Hub – including what I thought was most interesting, what I choose, and why. At the end I hope that, like Indiana Jones picking the Holy Grail from a murderers row of drinking vessels, I chose wisely.

Session 1 features conversations on Video Tracking, Data Lakes, the Lifecycle of an Analyst, Building Analytics Community, Sexy Dashboards (surely an oxymoron), Innovation, the Agile Enterprise and Personalization. Fortunately, while I’d love to join both Twitch’s June Dershewitz to talk about Data Lakes and Data Swamps or Intuit’s Dylan Lewis for When Harry (Personalization) met Sally (Experimentation), I didn’t have to agonize at all, since I’m scheduled to lead a conversation on Machine Learning in Digital Analtyics. Still, it’s an incredible set of choices and represents just how much breadth there is to digital analytics practice these days.

Session 2 doesn’t make things easier. With topics ranging across Women in Analytics, Personalization, Data Science, IoT, Data Governance, Digital Product Management, Campaign Measurement, Rolling Your Own Technology, and Voice of Customer…Dang. Women in Analytics gets knocked off my list. I’ll eliminate Campaign Measurement even though I’d love to chat with Chip Strieff from Adidas about campaign optimization. I did Tom Bett’s (Financial Times) conversation on rolling your own technology in Europe this year – so I guess I can sacrifice that. Normally I’d cross the data governance session off my list. But not only am I managing some aspects of a data governance process for a client right now, I’ve known Verizon’s Rene Villa for a long time and had some truly fantastic conversations with him. So I’m tempted. On the other hand, retail personalization is of huge interest to me. So talking over personalization with Gautam Madiman from Lowe’s would be a real treat. And did I mention that I’ve become very, very interested in certain forms of IoT tracking? Getting a chance to talk with Vivint’s Brandon Bunker around that would be pretty cool. And, of course, I’ve spent years trying to do more with VoC and hearing Abercrombie & Fitch’s story with Sasha Verbitsky would be sweet. Provisionally, I’m picking IoT. I just don’t get a chance to talk IoT very much and I can’t pass up the opportunity. But personalization might drag me back in.

In the next session I have to choose between Dashboarding (the wretched state of as opposed to the sexiness of), Data Mining Methods, Martech, Next Generation Analytics, Analytics Coaching, Measuring Content Success, Leveraging Tag Management and Using Marketing Couds for Personalization. The choice is a little easier because I did Kyle Keller’s (Vox) conversation on Dashboarding two years ago in Europe. And while that session was probably the most contentious DA Hub group I’ve ever been in (and yes, it was my fault but it was also pretty productive and interesting), I can probably move on. I’m not that involved with tag management these days – a sign that it must be mature – so that’s off my list too. I’m very intrigued by Akhil Anumolu’s (Delta Airlines) session on Can Developers be Marketers? The Emerging Role of MarTech. As a washed-up developer, I still find myself believing that developers are extraordinarily useful people and vastly under-utilized in today’s enterprise. I’m also tempted by my friend David McBride’s session on Next Generation Analytics. Not only because David is one of the most enjoyable people that I’ve ever met to talk with, but because driving analytics forward is, really, my job. But I’m probably going to go with David William’s session on Marketing Clouds. David is brilliant and ASOS is truly cutting edge (they are a giant in the UK and global in reach but not as well known here), and this also happens to be an area where I’m personally involved in steering some client projects. David’s topical focus on single-vendor stacks to deliver personalization is incredibly timely for me.

Next up we have Millennials in the Analytics Workforce, Streaming Video Metrics, Breaking the Analytics Glass Ceiling, Experimentation on Steroids, Data Journalism, Distributed Social Media Platforms, Customer Experience Management, Ethics in Analytics(!), and Customer Segmentation. There are several choices in here that I’d be pretty thrilled with: Dylan’s session on Experimentation, Chip’s session on CEM and, of course, Shari Cleary’s (Viacom) session on Segmentation. After all, segmentation is, like, my favorite thing in the world. But I’m probably going to go with Lynn Lanphier’s (Best Buy) session on Data Journalism. I have more to learn in that space, and it’s an area of analytics I’ve never felt that my practice has delivered on as well as we should.

In the last session, I could choose from more on Customer Experience Management, Driving Analytics to the C-Suite, Optimizing Analytics Career-Oaths, Creating High-Impact Analytics Programs, Building Analytics Teams, Delivering Digital Products, Calculating Analytics Impact, and Moving from Report Monkey to Analytics Advisor. But I don’t get to choose. Because this is where my second session (on driving Enterprise Digital Transformation) resides. I wrote about doing this session in the EU early this summer – it was one of the best conversations around analytics I’ve had the pleasure of being part of. I’m just hoping this session can capture some of that magic. If I didn’t have hosting duties, I think I might gravitate toward Theresa Locklear’s (NFL) conversation on Return on Analytics. When we help our clients create new analytics and digital transformation strategies, we have to help them justify what always amount to significant new expenditures. So much of analytics is exploratory and foundational, however, that we don’t always have great answers about the real return. I’d love to be able to share thoughts on how to think (and talk) about analytics ROI in a more compelling fashion.

All great stuff.

We work in such a fascinating field with so many components to it. We can specialize in data science and analytics method, take care of the fundamental challenges around building data foundations, drive customer communications and personalization, help the enterprise understand and measure it’s performance, optimize relentlessly in and across channels, or try to put all these pieces together and manage the teams and people that come with that. I love that at a Conference like the Hub I get a chance to share knowledge with (very) like-minded folks and participate in conversations where I know I’m truly expert (like segmentation or analytics transformation), areas where I’d like to do better (like Data Journalism), and areas where we’re all pushing the outside of the envelope (IoT and Machine Learning) together. Seems like a wonderful trade-off all the way around.

In my last posts before the DA Hub, I described the first two parts of an analytics driven digital transformation. The first part covered the foundational activities that help an organization understand digital and think and decide about it intelligently. Things like customer journey, 2-tiered segmentation, a comprehensive VoC system and a unified campaign measurement framework form the core of a great digital organization. Done well, they will transform the way your organization thinks about digital. But, of course, thinking isn’t enough. You don’t build culture by talking but by doing. In the beginning was the deed. That’s why my second post dealt with a whole set of techniques for making analytics a constant part of the organization’s processes. Experimentation driven by a comprehensive analytics-driven testing plan, attribution and mix modelling, analytic reporting, re-survey, and a regular cadence of analytics driven briefings make continuous improvement a reality. If you take this seriously and execute fully on these first two phases, you will be good at digital. That’s a promise.

But as powerful, transformative and important as these first two phases are, they still represent only a fraction of what you can achieve with analytics driven-transformation. The third phase of analytics driven transformation targets areas where analytics changes the way a business operates, prices its products, communicates with and supports its customers.

The third phase of digital transformation is unique. In some ways, it’s easier than the first two phases. It involves much less organization and cultural transformation. If you done those first two phases, you’re already there when it comes to having an analytics culture. On the other hand, in this third phase the analytics projects themselves are often MUCH more complex. This is where we tackle big hard problems. Problems that require big data, advanced statistical analysis, and serious imagination. Well, that’s the fun stuff. Seriously, if you’ve gotten through the first two phases of an analytics transformation successfully, doing the projects in Phase Three is like a taking a victory lap.

There isn’t one single blueprint for the third phase of an analytics driven transformation. The work that gets done in the first two phases is surprisingly similar almost regardless of the industry or specific business. I suppose it’s like laying the foundation for a building. No matter what the building looks like, the concrete block at the bottom is going to look pretty much the same. At this third level, however, we’re above the foundation and what you do will depend mightily on your specific business.

I know that it depends on your business is not much of an answer. As a consultant, it’s not unusual to get caught up in conversations like this:

“So how much would it cost?”

“Well, that depends.”

“What kind of things does it depend on?”

“Well, it depends on how deeply you want to go into it, who you want to have do it, and how you want to get it done.”

All of this is true, of course, but none of it is helpful. I usually try to short-circuit these conversations by presenting a couple of real world alternatives.

I think this is more helpful (though it’s also more dangerous). Similarly, when I present the third phase of an analytics driven transformation I try to make it specific to the business in question. And the more I know about the business, the more pointed, interesting, and – I hope – convincing that third phase is going to look. But if I haven’t spent much time a business, I still customize that third phase by industry – picking out high-level analytics projects that are broadly applicable to everyone in the sector.

That’s what I’m going to try to do here, with the added benefit of picking a couple different industries and showing how the differences play out in this third phase. Do keep in mind, though, that the description of this third phase – unlike that of the first two – is meant to be suggestive only. No real-world third phase (certainly no optimal one) is likely to mirror what I lay out here. It might not even be very close. What’s more, unlike the first phase (at least) which is close-ended (when you’ve done the projects I suggest you’re done with that phase), phase three is open-ended. You never stop doing analytics projects at this level. And that’s a good thing.

For the first example, I decided to start with a classic retail e-commerce view of the world. It’s a sector where we all have, at the very least, a consumer’s understanding of how it works. There are many, many possible projects to choose from, but here are five I often present as a typical starting point.

The first is an analytically driven personalization program. With journey-mapping, 2-tiered segmentation and a robust experimentation program, an enterprise should be a in a good position to drive personalization. Most personalization programs bootstrap themselves by starting with fairly straightforward segmentations (already done) and rule-based personalization decisions targeted to “easy” problems like email offers and returning visitors to the Website. That’s fine. The very best way to build a personalization program is organically – build it by doing it with increasing sophistication in more and more channels and at more and more touchpoints.

Merchandising optimization is another very big opportunity. So much of the merchandising optimization I see is focused on product detail pages. That’s fine as far as it goes, but it misses the much larger opportunity to optimize merchandising on search and aisle pages via analytics. Traditional merchandising folks have been slow to understand how critical moving merchandising upstream is to effective digital performance. This turns out to be analytically both very challenging and very rich.

Assortment optimization (and I might be just as likely to pick pricing or demand signals here) has long been a domain of traditional retail analytics. As such, I have to admit I didn’t think much about it until the last few years. But I’ve come to believe that digital analytics can yield powerful preference information that is typically missing in this analysis. To do effective assortment optimization, you need to understand customer’s potential replacement options. In the offline world, this usually involves making simple guesses based on high-level product sales about which products will be substituted. Using online view data, we can do much, much better. This is a case where digital analytics doesn’t so much replace an existing technique as deepen and enrich it with data heretofore undreamed of. Assortment optimization with digital data gives you highly segmented, localized data about product substitution preferences. It’s a lot better.

I’ve become a strong advocated for a fundamental re-think of loyalty programs based on the idea that surprise-based loyalty with no formal earning system is the future of rewards programs. The advantages of surprise-based loyalty are considerable when stacked up against traditional loyalty programs. You can target rewards where you think they will create lift. You can take advantage of inventory problems or opportunities. You don’t incur ANY financial obligations. You create no customer resentment or class issues. You can scale them and localize them to work with a specially trained staff. And, of course, the biggest bonus of all – you actually create far more impact per dollar spent. Surprise-based loyalty is, inherently, analytic. You can’t really do it any other way. Where it’s an option, it’s always one of the biggest changes you can make in the way your business works.

Finally, I’ve picked digital/store integration as my fifth project for analytics-led transformation. There are a number of different ways to take this. The drives between store and site are complex, important and fruitful. Optimizing those drives should be one of the analytics priorities for any omni-channel retail. And that optimization is a combination of testing and analytics. In this case, however, I’ve chosen to focus on measuring and optimizing digital in-store experiences. You’re surely familiar with endless-aisle retail; where digital is integrated into the in-store experience. The vast majority of these physical-digital experiences have been quite ineffective. Almost always, they’ve been executed from a retail perspective. By which I mean that they’ve been built once, dropped into the store, and left to fail. That’s just not doing it right. In-store experiences are getting more digital. Digital signage is growing rapidly. Physical-digital experiences are increasingly common. But if you want actual competitive advantage out of these experiences, you’d better tackle them from a digital test-and-learn/analytics perspective. Anything less is a prescription for failure.

So here’s my first round of Phase Three projects for an analytics driven transformation in retail. Each is big, complex and hard. They are also important. These are the projects that will truly transform your digital business. They are rubber-meets-the-road stuff that drive competitive advantage. It would be a mistake to try and execute on projects like this without first creating a strong analytics foundation in the organization. You’re chances of misfiring on doing or operationalizing the analytics are simply too great without that foundation. But if you don’t move past the first two phases into analytics like this, you’re missing the big stuff. You can churn out lots of incremental improvement in digital without ever touching projects like these. Those incremental improvements aren’t nothing. They may be valuable enough to justify your time and money. But if that’s all you ever do, you’ll likely find yourself wondering if it was all really worth it. Do any of these projects successfully, and you’ll never ask that question again.

Next week I’ll show a different (non-retail) set of projects and break-down what the differences tell us about how to make analytics a strategic asset.

I spent most of the last week at the fourth annual Digital Analytics Hub Conference outside London, talking analytics. And talking. And talking. And while I love talking analytics, thank heavens I had a few opportunities to get away from the sound of my own voice and enjoy the rather more pleasing absence of sounds in the English countryside.

With X Change no more, the Hub is the best conference going these days in digital analytics (full disclosure – the guys who run it are old friends of mine). It’s an immensely enjoyable opportunity to talk in-depth with serious practitioners about everything from cutting edge analytics to digital transformation to traditional digital analytics concerns around marketing analytics. Some of the biggest, best and most interesting brands in Europe were there: from digital and bricks-and-mortar behemoths to cutting-edge digital pure-plays to a pretty good sampling of the biggest consultancies in and out of the digital world.

As has been true in previous visits, I found the overall state of digital analytics in Europe to be a bit behind the U.S. – especially in terms of team-size and perhaps in data integration. But the leading companies in Europe are as good as anybody.

Here’s a sampling from my conversations:

Machine Learning

I’ve been pushing my team to grow in the machine learning space using libraries like TensorFlow to explore deep learning and see if it has potential for digital. It hasn’t been simple or easy. I’m thinking that people who talk as if you can drop a digital data set into a deep learning system and have magic happen have either:

Never tried it

Been trying to sell it

We’ve been having a hard time getting deep learning systems to out-perform techniques like Random Forests. We have a lot of theories about why that is, including problem selection, certain challenges with our data sets, and the ways we’ve chosen to structure our input. I had some great discussions with hardcore data scientists (and some very bright hacker analysts more in my mold) that gave me some fresh ideas. That’s lucky because I’m presenting some of this work at the upcoming eMetrics in Chicago and I want to have more impressive results to share. I’ve long insisted on the importance of structure to digital analytics and deep learning systems should be able to do a better job parsing that structure into the analysis than tools like random forests. So I’m still hopeful/semi-confident I can get better results.

In broader group discussion, one of the most controversial and interesting discussions focused on the pros-and-cons of black-box learning systems. I was a little surprised that most of the data scientist types were fairly negative on black-box techniques. I have my reservations about them and I see that organizations are often deeply distrustful of analytic results that can’t be transparently explained or which are hidden by a vendor. I get that. But opacity and performance aren’t incompatible. Just try to get an explanation of Google’s AlphaGo! If you can test a system carefully, how important is model transparency?

So what are my reservations? I’m less concerned about the black-boxness of a technique than I am its completeness. When it comes to things like recommendation engines, I think enterprise analysts should be able to consistently beat a turnkey blackbox (or not blackbox) system with appropriate local customization of the inputs and model. But I harbor no bias here. From my perspective it’s useful but not critical to understand the insides of a model provided we’ve been careful testing to make sure that it actually works!

Another huge discussion topic and one that I more in accord with was around the importance of not over-focusing on a single technique. Not only are there many varieties of machine learning – each with some advantages to specific problem types – but there are powerful analytic techniques outside the sphere of machine learning that are used in other disciplines and are completely untried in digital analytics. We have so much to learn and I only wish I had more time with a couple of the folks there to…talk!

New Technology

One of the innovations this year at the Hub was a New Technology Showcase. The showcase was kind of like spending a day with a Silicon Valley VC and getting presentations from the technology companies in their portfolio (which is a darn interesting way to spend a day). I didn’t know most of the companies that presented but there were a couple (Piwik and Snowplow) I’ve heard of. Snowplow, in particular, is a company that’s worth checking out. The Snowplow proposition is pretty simple. Digital data collection should be de-coupled from analysis. You’ve heard that before, right? It’s called Tag Management. But that’s not what Snowplow has in mind at all. They built a very sophisticated open-source data collection stack that’s highly performant and feeds directly into the cloud. The basic collection strategy is simple and modern. You send json objects that pass a schema reference along with the data. The schema references are versioned and updates are handled automatically for both backwardly compatible and incompatible updates. You can pass a full range of strongly-typed data and you can create cross-object contexts for things like visitors. Snowplow has built a whole bunch of simple templates to make it easier for folks used to traditional tagging to create the necessary calls. But you can pass anything to Snowplow – not just Web data. It’s very adaptable for mobile (far more so than traditional digital analytics systems) and really for any kind of data at all. Snowplow supports both real-time and batch – it’s a true lambda architecture. It seems to do a huge amount of the heavy lifting for you when it comes to creating a modern cloud-based data collection system. And did I mention it’s open-source? Free is a pretty good price. If you’re looking for an independent data collection architecture and are okay with the cloud, you really should give it a look.

Cloud vs. On-Premise

DA Hub’s keynote featured a panel with analytics leaders from companies like Intel, ASOS and the Financial Times. Every participant was running analytics in the cloud (with both AWS and Azure represented though AWS had an unsurprising majority). Except for barriers around InfoSec, it’s unclear to me why ANY company wouldn’t be in the cloud for their analytics.

Rolling your own Technology

We are not sheep

Here in the States, there’s been widespread adoption of open-source data technologies (Hadoop/Spark) to process and analyze digital data. But while I do see companies that have completely abandoned traditional SaaS analytics tools, it’s pretty rare. Mostly, the companies I see run both a SaaS solution to collect data and (perhaps) satisfy basic reporting needs as well as an open-source data platform. There was more interest in the people I talked to in the EU about a complete swap out including data collection and reporting. I even talked to folks who roll most of the visualization stack themselves with open-source solutions like D3. There are places where D3 is appropriate (you need complete customization of the surrounding interface, for example, or you need widespread but very inexpensive distribution), but I’m very far from convinced that rolling your own visualization solutions with open-source is the way to go. I would have said that same thing about data collection but…see above.

Digital Transformation

I had an exhilarating discussion group centered around digital transformation. There were a ton of heavy hitters in the room – huge enterprises deep into projects of digital transformation, major consultancies, and some legendary industry vets. It was one of the most enjoyable conference experiences I’ve ever had. I swear that we (most of us anyway) could have gone on another 2 hours or more – since we just scratched the surface of the problems. My plan for the session was to cover what defines excellence in digital (what do you have to be able to do digital well), then tackle how a large-enterprise that wants to transform in digital needs to organize itself. Finally, I wanted to cover the change management and process necessary to get from here to there. If you’re reading this post that should sound familiar!

It’s a long path

Well, we didn’t get to the third item and we didn’t finish the second. That’s no disgrace. These are big topics. But the discussion helped clarify my thinking – especially around organization and the very real challenges in scaling a startup model into something that works for a large enterprise. Much of the blending of teams and capabilities that I’ve been recommending in these posts on digital transformation are lessons I’ve gleaned from seeing digital pure-plays and how they work. But I’ve always been uncomfortably aware that the process of scaling into larger teams creates issues around corporate communications, reporting structures, and career paths that I’m not even close to solving. Not only did this discussion clarify and advance my thinking on the topic, I’m fairly confident that it was of equal service to everyone else. I really wish that same group could have spent the whole day together. A big THANKS to everyone there, you were fantastic!

I plan to write more on this in a subsequent post. And I may drop another post on Hub learnings after I peruse my notes. I’ve only hit on the big stuff – and there were a lot of smaller takeaways worth noting.

See you there!

As I mentioned in my last post, the guys who run DA Hub are bringing it to Monterey, CA (first time in the U.S.) this September. Do check it out. It’s worth the trip (and the venue is pretty special). I think I’m on the hook to reprise that session on digital transformation. And yes, that scares me…you don’t often catch lightning in a bottle twice.

I spent most of the last week on holiday in Italy. But since the holiday was built around a speaking gig in Italy at the Be Wizard Digital Marketing conference I still spent a couple of days talking analytics and digital. A couple of days I thoroughly enjoyed. The conference closed with a Q&A for a small group of speakers and while I got a few real analytics questions it felt more like a meet and greet – with plenty of puff-ball questions like “what word would use to describe the conference?” A question I failed miserably with the very pathetic answer “fun”.

I guess that’s why it’s better to ask me analytics questions.

The word I probably should have chosen is “gelato”.

And not just because I hogged down my usual totally ridiculous amount of fragola, melone, cioccolato, and pesca – scoop by scoop from Rimini to Venice.

Gelato because I had a series of rich conversations with Mat Sweezey from Salesforce (nee Pardot) who gave a terrific presentation on authenticity and what it means in this new digital marketing world. It’s easy to forget how dramatically digital has changed marketing and miss some of the really important lessons from those changes. Mat also showed me a presentation on agile that blends beautifully with the digital transformation story I’ve been trying to tell in the last six months. It’s a terrific deck with some slides that explain why test&learn and agile methods work so much better than traditional methods. It’s a presentation with the signal virtue of taking very difficult concepts and making them not just clear but compelling. That’s hard to do well.

Gelato because I also talked with and enjoyed a great presentation from Chris Anderson of Cornell. Chris led a two-hour workshop in the revenue management track (which happens to be a kind of side interest of mine). His presentation focused on the impact of social media content on sites like TripAdvisor on room pricing strategies. He’s done several compelling research projects with OTAs (Online Travel Agents) looking at the influence of social media content on buying decisions. His research has looked at key variables that drive influence (number of reviews and rating), how sensitive demand is to those factors, and how that sensitivity plays out by hotel class (turns out that the riskier the lodging decision the more impactful social reviews are). He’s also looked at review response strategies on TripAdvisor and has some compelling research showing how review response can significantly improve ratings outcomes but how it’s also possible to over-respond. Respond to everything, and you actually do worse than if you respond to nothing.

That’s a fascinating finding and very much in keeping with Mat’s arguments around authenticity. If you make responding to every social media post a corporate policy, what you say is necessarily going to sound forced and artificial.

That’s why it doesn’t work.

If you’re in the hospitality industry, you should see this presentation. In fact, there are lessons here for any company interested in the impact of reviews and social content and interested in taking a more strategic view of social outreach and branding. I think Chris’ data suggest significant and largely unexplored opportunities for both better revenue management decisions around OTA pricing and better strategies around the review ask.

Gelato because there was one question I didn’t get to answer that I wanted to (and somehow no matter how much gelato I consume I always want a little more).

Since I had to have translations of the panel questions at the end, I didn’t always get a chance to respond. Sometimes the discussion had moved on by the time I understood the question! And one of the questions – how can companies compete with publishers when it comes to content creation – seemed to me deeply related to both Mat and Chris’ presentations.

Here’s the question as I remember it:

If you’re a manufacturer or a hotel chain or a retailer, all you ever hear in digital marketing is how content is king. But you’re not a content company. So how do you compete?

The old-fashioned way is to hire an agency to write some content for you. That’s not going to work. You won’t have enough content, you’ll have to pay a lot for it, and it won’t be any good. To Mat’s point around authenticity, you’re not going to fool people. You’re not going to convince them that your content isn’t corporate, mass-produced, ad agency hack-work. Because it is and because people aren’t stupid. Building a personalization strategy to make bad content more relevant isn’t going to help much either. That’s why you don’t make it a corporate policy to reply to every review and why you don’t write replies from a central team of ad writers.

Stop trying to play by the old rules.

Make sure your customer relations, desk folks, and managers understand how to build relationships with social media and give them the tools to do it. If you want authentic content, find your evangelists. People who actually make, design, support or use your products. Give them a forum. A real one. And turn them loose. Find ways to encourage them. Find ways to magnify their voice. But turn them loose.

You can’t have it both ways. You can’t be authentic while you try to wrap every message in a Madison Avenue gift wrapping bought from the clever folks at your ad agency. Check out Mat’s presentation (he’s a Slideshare phenom). Think about the implications of unlimited content and the ways we filter. Process the implications. The world has changed and the worst strategy in the world is to keep doing things the old way.

So gelato because the Be Wizard conference, like Italy in general, was rich, sweet, cool and left me wanting to hear (and say) a bit more!

And speaking of conferences, we’re not that far away from my second European holiday with analytics baked in – The Digital Analytics Hub in London (early June). I’ve been to DA Hub several years running now – ever since two old friends of mine started it. It’s an all conversational conference modeled on X Change and it’s always one of the highlights of my year. In addition to facilitating a couple conversations, I’m also going to be leading a very deep-dive workshop into digital forecasting. I plan to walk through forecasting from the simplest sort of forecast (everything will stay the same) to increasingly advanced techniques that rely, first on averages and smoothing, and then to models. If you’re thinking about forecasting, I really think this workshop will be worth the whole conference (and the Hub is always great anyway)…

If you’ve got a chance to be in London in early June, don’t miss the Hub.

People have struggled with this (big) data provider model but Factual feels like it’s found a real (and valuable) niche. Would love to see more of this grow since external data is a huge miss in most big data systems.

Targeted VoC is a powerful (and totally neglected) tool for personalization. Facebook’s experience is entirely relevant to ANY content producer. I don’t know if I can take credit for this, but I suggested this to folks at Facebook a couple of years back!

An interesting discussion of the problems in identifying “likely” voters and the benefits of behavioral data integration. Food for thought in the enterprise world as well where the equivalent is often possible but rarely done.